Label entropy‐based cooperative particle swarm optimization algorithm for dynamic overlapping community detection in complex networks

Abstract

The real-world complex networks, such as biological, transportation, biomedical, web, and social networks, are usually dynamic and change over time. The communities which reflect the substructures hidden in the networks usually overlap each other, and detecting overlapping communities in the dynamic complex networks is a challenging task. Prior researchers have applied multiobjective optimization method to the detection of dynamic overlapping communities and achieved some excellent results. However, in terms of multiobjective processing, the prior studies all adopt the decomposition method based on weight parameters, and different weight parameters or different parameter values can easily affect the community detection results which further results in the uneven distribution of the detected results in the target space. To solve the above problems, a hybrid algorithm, that is, Collaborative Particle Swarm multiobjective Optimization-based Dynamic Overlapping Community Detection (CPSO-DOCD) algorithm is proposed in this paper. First, to improve the diversity of particles, the encoding/decoding of the particle and the cross inheritance and the variation of particle are redefined first based on label propagation. In each network snapshot, multiple particle swarms are initialized based on Community Overlap Propagation Algorithm (COPRA) to generate particles with uniform distribution. Multiple different objective functions are optimized using multiple particle swarms respectively to avoid the incorrect selection of weight parameters. In addition, a reference-point-based is adopted in the particle selecting stage to solve the uneven distribution of detected results in the target space. Second, a node label entropy-based particle swarm algorithm is proposed to improve the accuracy of community detection of current network snapshots. Finally, when one snapshot switches to another over time, a migration strategy based on COPRA local-search and clique generation is utilized to adjust the prior community detection results, which enables the former results can be adapted to the new network snapshots. The experiments are implemented based on four dynamic networks which are Cit-HepPh, Cit-HepTh, Emailed-EU-core-temporal, and CollegeMsg. The hypervolume value of the overlapping community detection result obtained by CPSO-DOCD is 0.5%–2% higher than MDOA, MCMOEA, SLPAD, and iLCD. Furthermore, CPSO-DOCD also performed better than MDOA, MCMOEA, SLPAD, and iLCD on C-metric values, and CPSO-DOCD can approach approximately to the Pareto frontier.